In this article, we will discuss the image and how to find a binary pattern using the pixel value of the image. As we all know, image is also known as a set of pixels. When we store an image in computers or digitally, it’s corresponding pixel values are stored. So, when we read an image to a variable using OpenCV in Python, the variable stores the pixel values of the image. As we can see in following example:
Python3
import cv2image = cv2.imread("GFG.jpg") # Now, the variable 'image' stores the pixel values of imageprint(image) |
Examples: Input : 
Output :
[[[255 255 255] [255 255 255] [255 255 255] ... [255 255 255] [255 255 255] [255 255 255]] [[255 255 255] [255 255 255] [255 255 255] ... [255 255 255] [255 255 255] [255 255 255]] [[255 255 255] [255 255 255] [255 255 255] ... [255 255 255] [255 255 255] [255 255 255]] ... [[255 255 255] [255 255 255] [255 255 255] ... [255 255 255] [255 255 255] [255 255 255]] [[255 255 255] [255 255 255] [255 255 255] ... [255 255 255] [255 255 255] [255 255 255]] [[255 255 255] [255 255 255] [255 255 255] ... [255 255 255] [255 255 255] [255 255 255]]]
Pixel values of the image will be stored in the variable and below is a part of the NumPy array which stores the values.
Local Binary Pattern
There are lots of different types of texture descriptors are used to extract features of an image. Local Binary Pattern, also known as LBP, is a simple and grayscale invariant texture descriptor measure for classification. In LBP, a binary code is generated at each pixel by thresholding it’s neighbourhood pixels to either 0 or 1 based on the value of the centre pixel. The rule for finding LBP of an image is as follows:
- Set a pixel value as center pixel.
- Collect its neighbourhood pixels (Here I am taking a 3 x 3 matrix so; total number of neighbourhood pixel is 8)
- Threshold it’s neighbourhood pixel value to 1 if its value is greater than or equal to centre pixel value otherwise threshold it to 0.
- After thresholding, collect all threshold values from neighbourhood either clockwise or anti-clockwise. The collection will give you an 8-digit binary code. Convert the binary code into decimal.
- Replace the center pixel value with resulted decimal and do the same process for all pixel values present in image.
Let’s take an example to understand it properly. Let’s take a pixel value from the above output to find its binary pattern from its local neighbourhood. So, I am taking a value ‘149’ (present at 15th row and 19nd column) and its 8 neighbourhood pixels to form a 3 x 3 matrix. 
Collect the thresholding values either clockwise or anti-clockwise. Here, I am collecting them clockwise from top-left. So, after collecting, the binary value will be as follows: 
Then, convert the binary code into decimal and place it at center of matrix.
1 x 27 + 1 x 26 + 1 x 25 + 0 x 24 + 0 x 23 + 0 x 22 + 0 x 21 +1 x 20 = 128 + 64 + 32 + 0 + 0 + 0 + 0 + 1 = 225
Now, the resulted matrix will look like, 
Now, let’s do it using python
Python3
import cv2import numpy as npfrom matplotlib import pyplot as plt def get_pixel(img, center, x, y): new_value = 0 try: # If local neighbourhood pixel # value is greater than or equal # to center pixel values then # set it to 1 if img[x][y] >= center: new_value = 1 except: # Exception is required when # neighbourhood value of a center # pixel value is null i.e. values # present at boundaries. pass return new_value # Function for calculating LBPdef lbp_calculated_pixel(img, x, y): center = img[x][y] val_ar = [] # top_left val_ar.append(get_pixel(img, center, x-1, y-1)) # top val_ar.append(get_pixel(img, center, x-1, y)) # top_right val_ar.append(get_pixel(img, center, x-1, y + 1)) # right val_ar.append(get_pixel(img, center, x, y + 1)) # bottom_right val_ar.append(get_pixel(img, center, x + 1, y + 1)) # bottom val_ar.append(get_pixel(img, center, x + 1, y)) # bottom_left val_ar.append(get_pixel(img, center, x + 1, y-1)) # left val_ar.append(get_pixel(img, center, x, y-1)) # Now, we need to convert binary # values to decimal power_val = [1, 2, 4, 8, 16, 32, 64, 128] val = 0 for i in range(len(val_ar)): val += val_ar[i] * power_val[i] return val path = 'GFG.png'img_bgr = cv2.imread(path, 1) height, width, _ = img_bgr.shape # We need to convert RGB image # into gray one because gray # image has one channel only.img_gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) # Create a numpy array as # the same height and width # of RGB imageimg_lbp = np.zeros((height, width), np.uint8) for i in range(0, height): for j in range(0, width): img_lbp[i, j] = lbp_calculated_pixel(img_gray, i, j) plt.imshow(img_bgr)plt.show() plt.imshow(img_lbp, cmap ="gray")plt.show() print("LBP Program is finished") |
Output: 
The output shown in examples contains some values in its X-axis and Y-axis which refers to the width and height of the input image respectively.
